The project combines IoT technologies, artificial intelligence, and advanced sensors to optimise agricultural management and promote sustainable practices in the sector.

CT announces the successful completion of the ALARAD project, an international initiative aimed at developing a smart farming platform for traceable, high-quality agricultural production. This project has facilitated the implementation of a decision-making support system based on proximal and remote sensing, driving the digital transformation of the agricultural sector and laying the groundwork for more productive, sustainable, and high-quality precision agriculture.

In Spain, the project concluded with the installation of a comprehensive monitoring and decision-making system in the vineyards of Bodegas Bohórquez, located in the Ribera del Duero Designation of Origin in Valladolid province.

Technological innovation at the service of agriculture

ALARAD integrates IoT technologies, artificial intelligence, and precision farming techniques to optimise vineyard management and maximise harvest yield and quality. In the vineyards of Bodegas Bohórquez, three sensor networks have been installed in different locations, providing real-time data accessible via a web application. This system monitors key variables such as the physiological state, vigour, and growth of the crop.

Additionally, CT’s team has developed advanced artificial intelligence models capable of predicting production-related variables such as yield per hectare, fruit pH, and other quality indicators critical for the Ribera del Duero Designation of Origin.

International collaboration with a local impact

The project is the result of international collaboration between South Korea’s Korea Electronics Technology Institute (KETI) and Spanish partners led by CT, within the framework of the Spain-Korea bilateral call. This joint effort enabled the development of two use cases: one in vertical farms in Korea and another in the vineyards of Bodegas Bohórquez in Spain.

On its side, CT led the design of the sensor architecture and data analysis, while its partner, Air Institute, contributed generalisable hardware and software solutions tailored for small-scale producers with limited broadband access.

The project also promotes sustainable vineyard management practices, focusing on traceability and reducing environmental impact, aligning with global agricultural sustainability goals.

Looking ahead: applications and potential

Not only has ALARAD demonstrated its effectiveness in vineyards, but its technology can also be applied to a wide variety of crops. The system gathers extensive data volumes, paving the way for future predictive models and customised optimisations for agricultural operations.

This innovative solution not only boosts crop productivity and quality but also supports data-driven decision-making, mitigates risks, and minimises environmental impact, heralding a new era in precision agriculture.

About ALARAD

The ALARAD Project is an R&D initiative under the bilateral call for the 2021 KRESIP Spain-Korea international programme in Artificial Intelligence, partially funded by the CDTI under reference IDI-20210939.

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The model is designed to increase the productivity of factories that work with constant variability in their production.

The initiative is funded by the TransMisiones program of the Ministry of Science, Innovation and Universities of the Government of Spain.

Mass customization is a strategy that enables companies to tailor their products to specific customer needs while maintaining low costs and high efficiency. However, this approach poses significant challenges to traditional automation strategies, which often require large investments and long payback periods.

In order to overcome these limitations, the ADAPTA project seeks to create a flexible and reconfigurable production model that provides factories with a high capacity for adaptation and resilience to changes in the environment. To this end, the project will seek to improve the perception capabilities of current robotic systems through solutions based on vision and artificial intelligence (AI), integrated sensors and 2D and 3D images.

The initiative will also work on developing handling systems that can adapt to unknown or changing situations with minimal human intervention and that take into account the presence of people and their interaction with them, favoring a collaborative productive environment.

As part of a multidisciplinary consortium led by Tekniker, CT is working along two key lines to address the challenges of mass customization of products in industrial environments. This approach seeks to provide innovative tools that enable factories to adapt quickly to changing market demands, maximizing efficiency and reducing costs.

On the one hand, the company focuses on analyzing the bidirectional relationship between the elements of an industrial factory, with the aim of facilitating an agile reconfiguration of the production process. This effort is part of the model-based system engineering (MBSE) concept, which promotes advanced virtualization of production plants.

In addition, CT experts are developing a digital twin designed to meet the needs of this reconfiguration. This system, conceived as software agnostic, will enable companies to anticipate different production scenarios and to quickly respond to dynamic situations required by customers.

The project, which has just started, is already making significant progress. The initial specifications have been defined in collaboration with the companies in the ADAPTA consortium, and the necessary know-how is being acquired to integrate the different software that will make up the virtualization of the factories. Although ADAPTA is expected to be developed over the next two years, CT is working on a tighter schedule to deliver the virtual foundations that will underpin the following consortium developments.

Validation in a real environment

The methodology proposed by the ADAPTA project includes the validation of the results obtained in an industrial scenario. These results will be provided by the company Schréder, project partner, in its luminaire assembly plant located in the province of Guadalajara, where the multinational group concentrates approximately 50% of its worldwide production.

Specifically, three representative use cases of handling, assembly and logistics operations will be tested in multiple production scenarios: the loading and unloading of product on a painting line, the collaborative assembly line and the autonomous loading of pallets of finished product onto trucks.

About ADAPTA

Funded by the TransMisiones program of the Ministry of Science, Innovation and Universities of the Government of Spain, the ADAPTA project has a consortium coordinated by Tekniker and integrated by Smarttech, CT Ingenieros de Catalunya Aeronáuticos, de Automoción e Industriales, Automatización de Sistemas y Aplicaciones Industriales (ASAI), División Industrial Artisteril, Bcnvision, Schréder Socelec, Eurecat and Universidad Carlos III de Madrid.

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CT is focused on developing AI-based solutions to redefine design and manufacturing engineering cycles in the aerospace industry.

The TIFON project (Intelligent Technologies for Manufacturing, Design and Operations in Industrial Environments) aims to explore and apply artificial intelligence technologies to optimize processes in the aeronautical industry, improving its sustainability, efficiency and responsiveness to future challenges.

A new paradigm for the aeronautical industry

TIFON focuses on identifying and solving the difficulties of applying AI technologies and their scalability to industrial processes (design and manufacturing) by executing pilots and proof-of-concepts. To this end, the companies in the consortium are addressing key areas of innovation, such as the automation of quality processes, the application of large language models (LLM) for knowledge management, and advanced techniques such as reinforcement learning and variational autoencoders (VAEs). These tools will not only make it possible to foresee defects and optimize processes, but also to foster collaboration between humans and machines, speeding up design and manufacturing cycles.

CT drives digital transformation and sustainability in the aerospace industry

Under this project, CT is focused on developing AI-based solutions to redefine design and manufacturing engineering cycles in the aeronautics industry. Its efforts include the application of machine vision to improve quality, the development of surrogate models and disruptive geometries for zero-emission aircraft, and advanced structural optimization techniques.

• Advanced structural dimensions: Use of neural networks for sizing structural elements, integrating them into the overall design of zero-emission aircraft.
• Inspection in hard-to-reach areas: Innovative methods to detect FOD (foreign object debris/damage) and evaluate large internal and external surfaces of aircraft.
• Thermal optimization through AI: Design of disruptive geometries for heat exchangers, optimized through generative modeling and additive manufacturing.
• Multidisciplinary structural optimization: Development of coherent and sustainable models that integrate structural and aerodynamic aspects to minimize overall aircraft weight.
• Advanced mechanical properties prediction: AI-based methodologies to predict mechanical properties of composite materials by designing unconventional stacking sequences and manufacturing parameters. These models will allow customizing materials and feeding optimal structural simulations.
• Predictive fatigue and crack assessment: surrogate models for predicting fatigue crack nucleation and propagation with random geometries and loads, integrating damage tolerance data into neural network methodologies.

CT is also investigating new AI-based strategies for predicting mechanical properties in composite materials, damage tolerance assessments, and generative design methodologies to explore more efficient and disruptive structural configurations.

A collaborative effort

The TIFON project is led by Airbus Defence & Space within the CDTI cluster, in collaboration with Navantia, 4I Intelligent Insights, Bertrandt and CT. In addition, the AEI cluster, coordinated by the Universidad Politécnica de Madrid (UPM), includes the participation of AIMEN, BSC, INTA, UAH, UC3M and US, consolidating a multidisciplinary research and development ecosystem.

Support and development framework

TIFON is an initiative funded by the CDTI under the file MIG-20232039, within the 2023 “Science and Innovation Missions” grants, in the framework of the State Program for Scientific, Technical and Innovation Research 2021-2023. Its execution is scheduled between 2024 and 2026, positioning itself as a strategic project to catalyze digital transformation and sustainability in the aeronautical industry.

With TIFON, CT reaffirms its commitment to innovation and technological leadership, actively contributing to the evolution towards a more efficient, sustainable and competitive industry.

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The final demonstration of the system was conducted by printing sensors and tracks on parts and components selected by CT and AXTER, respectively.

CT, the leading engineering company in technological innovation throughout the product lifecycle, has successfully concluded the 3DELECPRINT R&D project, which aimed to develop a flexible integrated robotic platform for printing electronic sensors and/or wiring on complex rigid 3D geometries. The resulting pieces are made of various materials such as metal, composite, or ceramic, among others.

During the project, a printing system based on an ultrasonic spray nozzle head was used, a versatile technique that allows the use of a wide range of inks, perfect adaptability to the substrate’s geometry, and efficient use of resources, as it works with very low flow rates.

This initiative marks a milestone in 3D printing by achieving the following technical objectives:

• Select and mature a spray nozzle system for the target application capable of printing sensors and electrical tracks with precision equivalent to that obtained with dedicated machines.
• Define the types of inks to be used, for example, for printing temperature sensors, to suit the printing technology.
• Conduct relevant ink tests against some demonstration substrates to determine the surface engineering necessary to improve ink adhesion to the substrate (e.g., the application of atmospheric plasma).
• Define and select the sensors to be incorporated into the robot’s arm that will maintain the robot’s position and trajectory according to the electronic design (whether sensors or electrical tracks).
• Define the curing strategy for the printed inks on the substrates to keep the process fully automated.
• Define and implement the integration of the various system components, particularly: Mechanical and functional integration of the nozzle subsystem to the robot subsystem, mechanical and functional integration of position and trajectory sensors in the robotic system, mechanical and functional integration of ultraviolet curing to the robot system.
• Program advanced robot and nozzle control algorithms for the required electronic printing target (width, spacing, track thickness, etc.)
• Test and adjust the integrated platform using a defined protocol.

About 3DELEPRINT

The project (RTC-2016-5569-7) has been funded by the MINISTRY OF ECONOMY, INDUSTRY AND COMPETITIVENESS and the European Union, within the framework of the Retos-Colaboración call of the State Research, Development and Innovation Program Oriented to the Challenges of Society, as part of the State Plan for Scientific, Technical, and Innovation Research 2013-2016, with the main goal of promoting technological development, innovation, and quality research.

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• Product
• Manufacturing and assembly systems
• Digitalization and design tools
• Advanced exploration of “Zero Emissions” configurations and associated technologies.

Over its two-year duration, the ONEiRE project primarily investigated the entire rear part of the aircraft, including the pressure bulkhead, the entire rear fuselage and the tail stabilizers. The project also encompassed the study and definition of the wing-fuselage fairing.

Led by Airbus Operations, the consortium included CT, Tecnilógica Ecosystems, D3 APPLIED TECH, Empresarios Agrupados Internacional, and AERTEC. Together, they have successfully forged new design capabilities that will benefit the next generation of aircraft. The teams also crafted tools that ensure proper design from the aircraft sizing phase, enabling quick iteration of various configurations.

Moreover, CT has achieved significant advances in creating surrogate models, which approximate the relationship between design variables and their outcomes with a limited number of complete analyses. These models streamline the design process, avoiding costly computer analyses and enhancing multi-objective optimization efforts.

CT has explored the design space through three practical use cases: developing a multi-spar torsion box for aircraft lift elements using AI techniques; eliminating mechanical joints; carbonizing metal fittings using detailed FEM for virtual testing; and the ultra-compaction of mechanical elements, such as optimizing and manufacturing a heat exchanger through metal additive processes to reduce weight.

CT played a pivotal role in developing methodologies and tools for the early design phases of these aircraft. For this purpose, automatic optimization tools for aerostructures using AI-based calculation processes (surrogate models) and automations of finite element models and CATIA geometry, development of methodology to create virtual tests through detailed FEM models and methods to reduce equipment, specifically heat exchangers, optimized and manufactured through additive manufacturing.

About ONEiRE

This project received funding from the CDTI under the file number PTAG-20211008, as part of the 2021 call for proposals under the Strategic Sectoral Innovation Initiatives (“Aeronautical Technology Program”). This is within the broader Recovery, Transformation, and Resilience Plan, funded by Next Generation EU funds, including the Recovery and Resilience Facility, and is part of the State Program for Business Leadership in R&D&I, within the State Plan for Scientific, Technical, and Innovation Research 2017-2020.

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Using river currents to intercept waste before it reaches the sea offers an efficient solution to marine pollution, since it significantly reduces collection efforts. However, despite various technological explorations, a comprehensive commercial solution has yet to emerge.

The CleanCoastLine NGO has decided to confront this problem and has entrusted the mission of finding a technical solution to CT. As part of its commitment to sustainable development, CT started working on the design and development of a system to contain and capture debris in rivers, as well as other channels and marine environments.

This initiative has been named REPERA(Retrieving Plastic Ebb from Rivers Autonomously) and its main goal is to design and develop an experimental concept model of an autonomous system that efficiently recovers river waste by using physical barriers and air bubbles to detect, redirect and collect waste floating in the water before it reaches the ocean. This groundbreaking solution stands out for being:

• a system using a passive floating barrier that allows river traffic while retaining and redirecting plastics with an air bubble barrier;
• autonomous, leveraging artificial intelligence, machine learning, and deep learning technologies for remote control and predictive operations;
• highly efficient, powered by sustainable energy sources, and adept at detecting, collecting, and redirecting waste materials;
• scalable, this concept model would be the basis for future versions of the system;
• versatile, as the system can act as an air bubble column or physical barrier, or both, depending on the type of location where it is placed, such as inland waterways and estuaries, coastal maritime areas, etc.

The REPERA project is studying the needs and behavior of a complete system that encompasses everything from generating green energy to create the bubble curtain, to collecting and extracting the waste (which can include not only plastics but also certain types of oily waste), as well as the sensorization of equipment and the creation of a virtual model of the system.

The disruptive nature of the project lies in developing a solution that can predict various problems and facilitate decision-making based on artificial intelligence algorithms that read and analyze data obtained from sensors installed in the system’s components.

Furthermore, given that air, water and solid structures are involved, CT experts have used CFD (Computational Fluid Dynamics) strategies for numerical analysis/calculation and experimental testing, in order to replicate and analyze the multiphase fluid-structure interactions between the system and the environment in which it will operate.

The development of such a solution could revolutionize waste management and protect marine environments from the onslaught of pollution, demonstrating a promising avenue for environmental conservation and sustainable practices.

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From its Bilbao office, CT leads this Basque R&D initiative, which has already entered the development and fine-tuning phase of calculation and simulation tools, CAE.

CT presents the progress of the second year of work on the ADDHOC R&D initiative, related to the development of simple programming tools, CAD, and of parts focused on DED technologies (WAAM and LMD).

The CT team and its consortium partners are closer to achieving ADDHOC’s goals and to offering a solution for custom manufacturing of metal parts for applications with high mechanical performance, short manufacturing runs, and customised geometries using additive manufacturing technologies.

Marc Abellán, FEA/CAE engineer, explains that they have managed to “solve certain limitations of the Wire Arc Additive Manufacturing (WAAM) technique, a directed energy deposition process in which an electric arc welding source is used to continuously melt and deposit a filler material in the form of wire”.

The project exploits the opportunities of this technology that drastically reduces material costs (up to 10 times less than the same alloy in other powder additive manufacturing systems) and simplifies handling. The process can also be carried out on robotic cells and conventional automatic welding systems, which minimises investment and training costs.

However, it has some limitations, which mainly lie in the need to carry out a final machining of the part due to the near-net-shape obtained and in the control of stresses and distortions that are generated especially in massive parts.

“Due to this limitation, our main objective and progress has been the optimisation of the heat treatments necessary prior to manufacturing to achieve a good balance of properties and thereby eliminate or minimise residual stresses, all of which has been achieved with ANSYS Workbench software by performing a thermomechanical analysis”, adds Marc Abellán.

Future objectives are to carry out:

• a study of the structural integrity (fatigue life, fracture, creep, and corrosion) of the manufactured parts,
• a process and CAD/CAM tools simulation for the automatic manufacture of 2.5D (2D sections extruded along the z-axis) and 3D parts,
• the reduction of material consumption by 70% to 90% by developing advanced deposition strategies to reduce the required machining allowances and minimise distortions of the manufactured parts”,

In this project, CT’s team of experts, led by Cecilia de la Fuente, contributes the know-how obtained in the Haritive project, and is responsible for the development of dynamic CAD/CAM/CAE through the integration of process data in the initial theoretical calculations, as well as the development of software environments based on Big Data and Machine Learning strategies.

The initiative responds to several technological and market aspects, such as a growing demand for precision and quality of machined parts, increasingly demanding lead times and production times, and the much desired reduction of manufacturing costs in medium-high added value processes.

The consortium led by CT is formed by multidisciplinary companies with extensive experience in high deposition additive manufacturing projects acquired in previous Hazitek calls (HARITIVE, HARIPLUS, WAAMTOOL), including: ADDILAN, Donosti Soldadura, AIPC, ABD Compressors, Tecnalia Research and Innovation and the University of the Basque Country, as a collaborating entity.

ADDHOC is part of “Smart Industry”, one of the three strategic priorities included in the new “2030 Basque Science, Technology and Innovation Plan” (PCTI Euskadi 2030). The project is part of the Support Program for Business R&D – Hazitek, co-funded by the Basque Government and the European Union through the European Regional Development Fund 2021-2027 (ERDF).

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The project is coordinated by Innovacc and has received a grant from NextGenerationEU funds covering 77% of the budget. Other participating partners are companies from the food sector, such as Matadero Frigorífico Avinyó (D’Avinyó) and Catalana de Embutidos (Grup Roma), and technology companies such as CT, Nevitec Vision Technologies, as well as CIAC – Clúster de la Indústria d’Automoció de Catalunya.

Artificial vision and data science to minimize human error and logistics impact thourghout the whole product chain

The optimisation and automation of the processes in the food industry is growing exponentially, and the pork industry is no exception. The value chain of this sector is highly complex due to the different actors involved: livestock farms, transport agents, slaughterhouses, cutting rooms and distributors. For this reason, in-depth production control must be applied to avoid mistakes at any point in the chain. Different steps of the value chain will be addressed: cutting rooms (process and operations), distribution and transport (internal and external logistics).

In more detail and entering fully into the processes of the processing industry, in this case the cutting room, the meat traceability control and management systems are vital to identify and know the origin of the raw material, either by batch or by supplier. It is also very helpful to know the packaging and the container in which the meat is placed. This means that knowing the Kg of raw material that enter and exit the facilities will be especially important to optimise the process. In the logistics phase, which is one of the processes with the greatest impact on the meat industry, control the cold chain throughout the process is vitally important. And at every point in the value chain as well.

For this reason, this project is presented with the aim of researching new techniques to increase the control of foreign bodies in the cutting room and to maintain traceability from the cutting room to the end consumer (internal and external transport). The optimisation of logistics routes will also be affected. The solution will make it possible to optimise the production process and optimise the distribution of the producers themselves, reducing human error.

Project goals

The main goal of the project is to ensure the correct traceability of meat products from the moment they are cut, to the end consumer.

• Automate the point at which the operators are working so far.
• Ensure that there are no visible foreign bodies (plastic, gloves, etc.) on the surface of the boxes.
• Significantly reduce the environmental impact caused by the consumption of hydrocarbons, improving the quality of life of society.
• Accelerate digitalisation in the meat sector, boosting competitiveness at the national and international levels.
• Increase knowledge in enabling technologies 4.0.
• Change in the organisation of existing processes.
• Improvement in the planning and allocation of resources.
• Availability of historical and real-time data to design routes.
• Lower costs thanks to improved traceability and detection of foreign bodies.

Participants:

• Matadero Frigorífico de Aviñó SA
• Catalana de Embutidos SA
• Nevitec Vision Technologies SL
• CT Ingenieros de Catalunya, A.A.I SL
• CIAC – Clúster de la Indústria d’Automoció de Catalunya
• INNOVACC

About TRACVI: AEI2022b project, approved in the AEI 2022 NG grant line. “Optimising product traceability and management of land logistics routes using artificial vision and data science”.

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Simulation and digital twin for plant expansion

The digital simulation or model provides a digital twin of the plant, processes and resources, which in the first place makes it possible to size and verify the new plant’s distribution and resources, and once it is in operation, serves as a tool to validate the production plans, the incorporation of new processes or products, and the optimal sizing of production resources.

For this reason, to create and plan the new plant, CT has proposed the creation of a digital model of its processes and resources, as well as its internal logistics. This tool makes it possible to study several key aspects of plant operation: detection of bottlenecks, optimization of layout and process, warehouse stock and intermediate buffers, internal logistics, profitability and feasibility of incorporating automatic transport systems such as AGV’s in key areas, sizing of operators and shifts, the optimal distribution of operators and processes, studies of energy consumption.

Harnessing the potential of IoT to reinforce any type of targeted software

Systems integration is currently one of the most important trends to improve processes. This is what has also come to be called Business Intelligence (BI).

Within systems integration, there are many types of targeted software: the ERPs that manage the operational part, the MESs focused on production, the product-oriented PLMs, or the CRMs that are associated with the sales aspect.

In this case, taking into account the large amount of data collected and evaluated by the client, CT has detected an opportunity for the production process to benefit from IoT, in order to supervise with real-time information on the performance of the machines. The summary of all of the information obtained from the system (data, metrics and KPIs) is displayed to the client in a dashboard implemented with tools such as Power BI.

The smart, safer, more agile and flexible factory

The automotive industry is leader in terms of installation of Automated Guided Vehicles (AGV) and Autonomous Mobile Robots (AMR), intelligent mobile elements that can tow loads and moving autonomously inside or outside the facilities. Their performance and increased reliability, facilitated by optical systems such as LiDAR, have accelerated their application in vehicle manufacturing plants.

Some of the different aspects that CT addresses in its application are safety, geolocation and programming, keys to the robot-load interaction and to the plant’s infrastructure development needs.

Our proposals are based on multiple suppliers that are widely deployed in the region, so the support is guaranteed. All of these require an integrator, which helps with the unique adaptation of each case.

The ingredients of an efficient plan

At CT, we have all of the tools needed to create an efficient digitization plan and ensure digital continuity, where information flows in all of the processes of a product, from manufacturing to export. We also bring together the technical and human value of our Industry 4.0 experts, with extensive experience in projects for the leading OEMs and Tier 1, non-stop participation in international R&D projects, and relational capital in the national and international Innovation ecosystem, made up of universities, sector associations, technology centers and start-ups, among others.

Our constant monitoring of technology, competition and sales, combined with our ability to incubate new projects with agile methodologies (Design Thinking, Lean Start-up, etc.) help us to carry out a complete strategic business analysis to determine the suitability and deployment of empowering tools.

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CT has initiated the R&D project VAREAL which will develop a new platform for the simulation and 3D model to optimize industrial assembly processes by using augmented virtual reality.

Amongst the intended improvements in the project, there is the visualisation of the industrial process as well as the industrial components, a reduction in assembly process times, a greater understanding of complex processes which were only to be found in extensive manuals and a great traceability and access to information in real time. These improvements will, for example, revert to facilitating the operators work and detecting deficiencies or technical specifications.

The software that integrates augmented virtual reality is specifically designed for highly intensive assembly processes in the aeronautical, automobile and rail sectors.

The project is co-financed by the Ministry of Energy, Tourism and Telecommunications in the National Plan for Scientific Investigation, Development and Technological Innovation 2013-16 with the Registration no. TSI-100600-2017-27.

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We are also using Catia Manufacturing Solutions to digitalize various activities, to support the workshop and reduce the environmental footprint through paperless digital documentation. Our team specialized in immersive technologies and software development is helping to transfer the documentation from the current digital tablet format to Virtual Reality using the new Microsoft Hololens.

Throughout the process of conversion of the civil A330 aircraft into an MRTT we have overcome different challenges using different approaches, such as concurrent engineering, the industrialization of new processes and the standardization of methods and processes.

Our contribution in the conversion consisted of managing concurrence with other departments and providing graphical work instructions, workshop support, technical support for production and defining Catia Manufacturing Solutions. As a result, we optimized the time and resources used in the conversion, while building synergies between different areas throughout the entire process.

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We can help you to reduce time and costs spent on training and better maintain your fleet, by giving you direct access to all the information you need, such as logistics, technical manuals, maintenance management, historical maintenance and much more, all in a single tool.

You can manage, visualize and edit all this information directly from a web portal that can be accessed through a tablet or other AR device. Augmented or Virtual Reality applications can be used to guide technicians during maintenance tasks or to train them in a specific vehicle.

With your tablet or AR device you can overlap virtual information on a real vehicle that provides step by step instructions on how to do a specific maintenance task.

This application has also been developed for training purposes. You can see the different systems, their elements and navigate through them using Augmented Reality overlaid directly on the real vehicle.

You can also update the database directly through your handheld device, adding comments, pictures, or any other relevant information.

This tool was developed for a vehicle fleet in the military sector but can be easily be used in any other industry.

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